Many computer vision tasks, for example object recognition and registration require the segmentation of an image where regions of the image are provided with a label. Semantic Segmentation provides detailed pixel-level specification, which is particularly suited for applications which often require obstacle detection and accurate boundary detection. Such applications include but are not limited to autonomous vehicles and driver assistants, embedded and wearable devices.
Modern semantic segmentation methods achieve highly accurate results, but often at the cost of reduced efficiency. Recent development of convolutional neural networks (CNNs) marks significant improvement in the results achieved by these networks. Their effectiveness, however, is largely dependent on the number of operations and parameters involved in the model. Modern semantic segmentation methods require more than a second to perform object classification of a single image, even if the processing is performed on a high-end Graphic Processing Unit (GPU). The complexity of these methods hinders their deployment in real-time applications.
Autonomous driving is a complex task which not only requires object detection and recognition with high precision but also a real-time operation of the object classification neural network. Therefore, a new approach for semantic segmentation is required where the semantic segmentation is performed in real-time without compromising the precision of the object classification.